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The rampant growth of “peer-to-peer” transactions and the “sharing” economy have had a profound impact on transportation. The goal of companies like Uber and Lyft is to make ride-sharing so inexpensive and convenient that they become more economical than owning a car. And many companies recognize the transformative potential of self-driving cars, summoned on demand. This transformation could reduce congestion and the economic drag from it, increase consumer convenience and mobility, as well as improve safety, energy use, urban design, and reduce pollution. What evidence do we have on the effects of these shifts? What are the prospects for self-driving cars, as well as the technological and regulatory hurdles they face?

On December 5, the new Brookings Center on Regulation and Markets hosted an event releasing new research on the congestion reducing benefits of autonomous vehicles and the consumer surplus stemming from the sharing economy. Following the presentations, the authors participated in a panel discussion with other experts on the benefits, costs, and prospects for autonomous vehicles. After the panel, Rep. Earl Blumenauer delivered a keynote address focusing on what role Congress plays in transportation and how autonomous vehicles can fix the nation’s infrastructure.

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The rampant growth of “peer-to-peer” transactions and the “sharing” economy have had a profound impact on transportation. The goal of companies like Uber and Lyft is to make ride-sharing so inexpensive and convenient that they become more economical than owning a car. And many companies recognize the transformative potential of self-driving cars, summoned on demand. This transformation could reduce congestion and the economic drag from it, increase consumer convenience and mobility, as well as improve safety, energy use, urban design, and reduce pollution. What evidence do we have on the effects of these shifts? What are the prospects for self-driving cars, as well as the technological and regulatory hurdles they face?
On December 5, the new Brookings Center on Regulation and Markets hosted an event releasing new research on the congestion reducing benefits of autonomous vehicles and the consumer surplus stemming from the sharing economy. Following the presentations, the authors participated in a panel discussion with other experts on the benefits, costs, and prospects for autonomous vehicles. After the panel, Rep. Earl Blumenauer delivered a keynote address focusing on what role Congress plays in transportation and how autonomous vehicles can fix the nation’s infrastructure. The rampant growth of “peer-to-peer” transactions and the “sharing” economy have had a profound impact on transportation. The goal of companies like Uber and Lyft is to make ride-sharing so inexpensive and convenient that ... https://www.brookings.edu/research/tracking-the-gig-economy-new-numbers/Tracking the gig economy: New numbershttp://webfeeds.brookings.edu/~/210694896/0/brookingsrss/topics/transportation~Tracking-the-gig-economy-New-numbers/
Mon, 10 Oct 2016 21:11:24 +0000https://www.brookings.edu/?post_type=research&p=336571

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Nearly a decade after the founding of Uber and Airbnb it’s still hard to get a handle on the size and importance of either those particular platforms or the larger “gig economy.”1

On the one hand, the rise of Uber, Lyft, and Airbnb has generated so much controversy about online talent platforms, the changing nature of work, and workers’ rights that it has at times been hard to get a clear fix on the sector and its meaning.2

On the other hand, the sector’s size and growth has been difficult to clarify, because it has been difficult to measure. Government data-gathering, for example, has not been well positioned to capture the gig economy, in part because it is conceptually complex and in part because the government stopped counting “contingent workplace” arrangements after 2005.3

Which means that no comprehensive database exists on either employment in the gig economy or its geography.

Authors

As a result, debates have flared over the true size and significance of the sector. Some skeptics, by way of aggregate self-employment statistics, conclude that “proof of the revolution…is hard to find.” Others have worked directly with platform company data or leveraged other proprietary information to assess the size and nature of online gigging. Overall, these national analyses have tended to describe a small but rapidly growing realm of platform-enabled freelancing. So far, though, the findings have yet to be extended to city-by-city estimates of growth or comparisons of activity across metro areas.

However, it turns out that for all of the limitations of the available data, additional light can in fact be thrown on the online gig economy. Specifically, insight can be gleaned—if one knows where to look for it—from an obscure Census Bureau dataset on “nonemployer firms,” which tracks the activity of “businesses” that earn at least $1,000 per year in gross revenues (or $1 in construction) but employ no workers.4

As it happens, the vast majority of these “businesses”—up to 93 percent of them in the rides and rooms industries—turn out to be self-employed, unincorporated sole proprietors.5 In other words, they are individuals earning income by freelancing or contracting with other businesses such as Uber, Lyft, and Airbnb. All of which means that one can learn a lot by analyzing these “firms’” proliferation and location—especially since the data (derived from tax records at the Internal Revenue Service) are available at fairly detailed levels of industrial activity (NAICS codes) and geography (counties).6

And so one of us looked last year at one city (early-adopting San Francisco) and at two leading gig economy segments (rides and rooms) and found a substantial rise in platform “gigs” between 2009 (when uptake began) and 2013 (the latest year data was available). The platform economy was clearly trackable and substantial—if only in one unique city.7

Yet now, with another year of data available, it is possible to update and expand the initial analysis. Therefore, we look here at the two industries most closely associated with the online gig economy—peer-to-peer ride-sharing and peer-to-peer room-sharing—and assess gig activity both nationally and with a particular focus on the 50 largest metropolitan areas. For comparison, we benchmark these trends against trends in nonemployer firms for the entire economy, and against payroll employment in the requisite industries. In addition, we used corporate websites and local news outlets to confirm that Uber, Lyft, and Airbnb were in fact operating in the 50 large metro areas during the years examined. They were. Uber and Lyft were running in many of the 50 metros’ principal cities by 2012 and in most of them by 2013. Airbnb was operating in all of the metros by 2012.8

What, then, do we see in these data focused on the rides and rooms industries? Three major findings stand out:

The gig economy, as reflected by nonemployer firms, is significant and growing fast.

Overall, there has been a clear surge in nonemployer firms’ business activity in the last decade, which almost certainly reflects, at least in part, the rise of online platforms.

To begin with, nonemployer firms—though still relatively small in overall economic value historically (accounting for about 3 percent of total business receipts)—are becoming a more important factor in the entire economy, having come to encompass nearly 24 million “businesses” in 2014 up from 15 million in 1997 and 22 million in 2007. (By comparison, total U.S. payroll employment was about 145 million in 2014, up from 129 million in 1997). To be sure, some of the recent growth in nonemployer firms reflects the spread of myriad investment vehicles and other partnerships such as limited partnerships that are increasingly used for disbursing a particular pools of funds. However, self-employed, sole proprietor, unincorporated firms (that is, contractors and freelancers) constitute the lion’s share of this activity (seven of every eight nonemployer firms). In short, gig employment—whether digital platform enabled or not—has been growing.

Turning to the rides and room industries, the nonemployer firms data documents parallel, but differently scaled, increases in activity in the two industry segments associated with passenger ground transit after 2010 (when Uber launched in San Francisco) and in the two industries linked to traveler accommodation after 2008 (the year Airbnb opened). Granted, nonemployer firms in the taxi and limousine industry were a big part of the “rides” sector prior to the arrival of Uber and Lyft.9 However, after 2010, independent contractor growth in the ground transportation industry suddenly takes off—and then explodes in 2014 (a trend that is likely to have continued in 2015 and 2016). In that year the nonemployer firm growth rate in ride-sharing was 34 percent, compared with 4 percent for payroll employment in the industry. Between 2010 and 2014, nonemployer firms in ride-sharing grew by 69 percent while payroll employment grew by just 17 percent.

Trends in the “rooms” sector are similar but of a lesser magnitude. Nonemployer firms in the sector play a much more modest role in employment, which makes sense given how many employees it takes to operate a typical hotel. Nonemployer firms, on the other hand, contract out things like cleaning services, and meals and entertainment that will spill over into the local economy. Even so, nonemployer firms have been growing at a faster pace in the rooms business than has payroll employment, particularly after 2008 when Airbnb launched. An especially sharp jump is apparent in 2014—when more than one-half of the period’s growth occurs (specifically, a one-year increase of 9 percent). Moreover, home-sharing sectors are likely especially undercounted in the data, because of tax reporting requirements—which again, are not mandatory until a room-sharing host exceeds $20,000 in receipts or 200 transactions each year. In addition, recent reports suggest that many Airbnb hosts are not individuals, but businesses renting properties on a permanent basis—an activity that likely does not show up in the data.

In short, a surge of nonemployer firm activity—explosive in ground transportation and noticeable in accommodations—seems to directly coincide with the large-scale expansion of the gig economy and uptake of online platform services. Whereas payroll employment in the ride-sharing industry grew by 17 percent between 2010 and 2014, nonemployer firms (including gigging freelancers) increased by 69 percent. Likewise, although payroll employment in the home-sharing industry grew by 7 percent, nonemployer firms grew by 17 percent.

Of course, these numbers should be interpreted with caution. While the expansion of nonemployer firms in the two sectors coincides with widening adoption of these online platforms, we cannot say for sure that one caused the other, nor can we say that growth is entirely attributable to ride- and room-sharing apps. However, it is reasonable to conclude that, at a minimum, the emergence of these online platforms is contributing to the growth of the freelance and contractor workforce in a material way.

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Platform-based freelancing is not yet substantially displacing payroll employment—but that could change.

Despite the uptick in nonemployer contractors, payroll employment in “rides and rooms” industries has not declined during the last five years. Instead, payroll employment has increased in these industries, particularly in the passenger ground transit sectors.

Again, caution is due when interpreting the data as the underlying dynamics are complex. Nonetheless, the rides and rooms sectors saw 17 percent and 7 percent growth, respectively, in payroll employment between 2010 and 2014. This rise contradicts the widely held belief that platform-based freelancing has displaced wide swaths of existing businesses. Uber and Airbnb, at least between 2010 and 2014, were not in most cases driving traditional incumbent firms to lay off payroll workers or go out of business. Instead, these data lend credence to the contention that Uber and Airbnb are meeting unmet consumer demand or stimulating new demand through innovative service offerings.

Yet it is still early. Nonemployer firm growth is clearly rising, and accelerating in the ride-sharing sector. These trends raise the possibility that the online marketplaces could cannibalize competing payroll businesses in some industries, particularly given the rapid deployment of new technologies. For example, Lyft recently claimed that most of its cars will be self-driving in five years. While that forecast sounds overly-ambitious to us, its actualization would spell trouble for human drivers, both those on payrolls and freelancers alike. More detailed tracking of the industry will be necessary to ascertain how these dynamics play out.

Online gigging in the rides and rooms industries is so far concentrated in large metropolitan areas.

Turning to the geography of nonemployer firm growth, it appears that gig economy activity is unevenly distributed in the rides and rooms industries. To begin with, the spread of nonemployer firms between 2010 and 2014 occurred mostly in the largest metro areas. No less than 81 percent of the four-year net growth in nonemployer firms in the rides sector took place in the 25 largest metros, while 92 percent occurred in the largest 50 metros. For rooms, those figures were, respectively, 56 percent and 70 percent—just slightly more than it was across all industries. (It makes sense that rides would be more concentrated in urban areas than rooms, given that a larger share of the nation’s travel and stays in overnight accommodations lies outside of urban areas.)

A closer look at the distribution of nonemployer firm growth reveals a variegated map of often rapid growth. Focusing now on just the years 2012–2014 (when Uber, Lyft, and Airbnb were really catching on), growth is explosive for rides across most larger US metro areas, with all 50 large metros gaining activity. For rooms the growth is more muted but also noticeable.

Source: Brookings analysis of Census Bureau and Moody’s data. Note: Payroll employment has been suppressed for Tampa and Milwaukee.

Setting the pace of growth in the ride-sharing industry were seven tech-oriented, mostly Western, metro areas. These seven saw nonemployer-firm activity in the ride industry double during the two years, with San Jose and San Francisco leading the way, followed by Los Angeles, Austin, San Diego, and Nashville. More broadly, more than half (27) of the large metro areas saw 50 percent or better growth in nonemployer firms in ground transportation industries, with two-year growth rates ranging of 94 percent in Boston, 85 percent in Pittsburgh, and 82 percent in Seattle to 63 percent in DC, 61 percent in Indianapolis, and 52 percent in Providence. Lending credence to the suggestion that nonemployer-firm growth reflects gig economy growth is the fact that five of the slowest growing ten metros for nonemployer firms on the rides side are five of the six metros in which either Uber or Lyft had yet to launch during the study years.

By comparison, payroll employment growth in the industry was much more subdued, or negative, in 17 metro areas.10 In San Jose and Sacramento, where nonemployer firms in ground transportation grew by 145 and 92 percent, respectively, payroll employment declined by 31 percent and 22 percent, respectively. This evidence suggests that while peer-to-peer rides do not appear to be cannibalizing taxi drivers on payroll in a widespread fashion, the potential may be there in some markets. With that said, the present data do not allow formal analysis of whether displacement is occurring, so the coincidence of nonemployer firm growth and payroll decline should be interpreted with caution, as many other factors could be at play. After all, the shift to contractors in the rides industry began well before the arrival of Uber and Lyft. Meanwhile, the dynamics of individual markets vary widely and lie beyond the scope of this analysis.

Data on the rooms sector, meanwhile, reveals a more subdued story of change. Still, nonemployer firm growth was widespread, with five metro areas (Austin, San Francisco, Portland, New Orleans, and San Jose) registering 37 percent or better two-year firm growth, and 13 metros seeing gains of 20 percent or more. Payroll employment, meanwhile, has held steadier than in the rides industry.

Overall, the nonemployer firm data consulted here add to what is known about the development and implications of the online-enabled gig economy.

To be sure, the information here remains imperfect.

For one thing, the nonemployer firms data only allow analysis of potentially gig-driven changes in two industries, given that it remains a proxy measure for platform-based gigging. For another thing, the present analysis remains confined to just two precincts of the much larger gig economy, given that such activity is difficult to capture in more diffuse industry areas, such as crafts, errand-running, and task-fulfillment. For now, the conceptual blurriness of such activities precludes analysis of freelancing on platforms like Etsy, Taskrabbit, or Thumbtack.

Finally, the nonemployer firm trends presented here reflect lagged data, and do not include activity in 2015 and 2016. As such these data clearly underestimate the true amount of activity occurring on these platforms. Given this fact, it seems fair to say the trends noted here represent a lower-bound estimate of online gig growth in these two industries.

And yet, despite these flaws, the nonemployer firm data suggests that the online platform economy is showing up in the official statistics; that it is mostly an urban phenomenon, at least in rides and rooms, where it is having a sizable impact locally; and that its onset in early-adopter cities like San Francisco and San Jose is now extending to other large cities all across the nation. Granted, future analysis will need look more closely at the effects of the gig economy on payroll employment and wages over time. Whether platform-based gigging serves unmet consumer demand or cannibalizes it from payroll enterprises will be a critical question in the next few years. Likewise, additional work must tackle the size and growth of the gig economy in broader and harder-to-measure industries.

With that said, this analysis does provide a start at a solid cross-sectional picture.

In any event, it is good to know—in the absence of systematic, topic-specific government counts—that the nonemployer firm data can provide a plausible estimate of the growth and geography of some zones of the hard-to-measure gig economy. Though imperfect, these data, we believe, point in the direction that common sense suggests: The platform economy for rides and rooms is now sizable and growing rapidly in many larger metro areas.

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https://www.brookings.edu/wp-content/uploads/2016/10/metro_20161010_traffic.jpg?w=270Nearly a decade after the founding of Uber and Airbnb it’s still hard to get a handle on the size and importance of either those particular platforms or the larger “gig economy.”1
On the one hand, the rise of Uber, Lyft, and Airbnb has generated so much controversy about online talent platforms, the changing nature of work, and workers’ rights that it has at times been hard to get a clear fix on the sector and its meaning.2
On the other hand, the sector’s size and growth has been difficult to clarify, because it has been difficult to measure. Government data-gathering, for example, has not been well positioned to capture the gig economy, in part because it is conceptually complex and in part because the government stopped counting “contingent workplace” arrangements after 2005.3
Which means that no comprehensive database exists on either employment in the gig economy or its geography.
Authors
Ian Hathaway
Nonresident Senior Fellow - Metropolitan Policy Program
Mark Muro
Senior Fellow and Policy Director - Metropolitan Policy Program Twitter markmuro1
As a result, debates have flared over the true size and significance of the sector. Some skeptics, by way of aggregate self-employment statistics, conclude that “proof of the revolution…is hard to find.” Others have worked directly with platform company data or leveraged other proprietary information to assess the size and nature of online gigging. Overall, these national analyses have tended to describe a small but rapidly growing realm of platform-enabled freelancing. So far, though, the findings have yet to be extended to city-by-city estimates of growth or comparisons of activity across metro areas.
However, it turns out that for all of the limitations of the available data, additional light can in fact be thrown on the online gig economy. Specifically, insight can be gleaned—if one knows where to look for it—from an obscure Census Bureau dataset on “nonemployer firms,” which tracks the activity of “businesses” that earn at least $1,000 per year in gross revenues (or $1 in construction) but employ no workers.4
As it happens, the vast majority of these “businesses”—up to 93 percent of them in the rides and rooms industries—turn out to be self-employed, unincorporated sole proprietors.5 In other words, they are individuals earning income by freelancing or contracting with other businesses such as Uber, Lyft, and Airbnb. All of which means that one can learn a lot by analyzing these “firms’” proliferation and location—especially since the data (derived from tax records at the Internal Revenue Service) are available at fairly detailed levels of industrial activity (NAICS codes) and geography (counties).6
Related Content The Avenue
Manufacturing startups head for the cloud—and your city Mark Muro Tuesday, May 19, 2015 The Avenue
From advanced industries, a lesson about wages Jonathan Rothwell and Mark Muro Tuesday, March 10, 2015
And so one of us looked last year at one city (early-adopting San Francisco) and at two leading gig economy segments (rides and rooms) and found a substantial rise in platform “gigs” between 2009 (when uptake began) and 2013 (the latest year data was available). The platform economy was clearly trackable and substantial—if only in one unique city.7
Yet now, with another year of data available, it is possible to update and expand the initial analysis. Therefore, we look here at the two industries most closely associated with the online gig economy—peer-to-peer ride-sharing and peer-to-peer room-sharing—and assess gig activity both nationally and with a particular focus on the 50 largest metropolitan areas. For comparison, we benchmark these trends against trends in nonemployer firms for the entire economy, and ... Nearly a decade after the founding of Uber and Airbnb it’s still hard to get a handle on the size and importance of either those particular platforms or the larger “gig economy.”1
On the one hand, the rise of Uber, Lyft, and ... https://www.brookings.edu/events/u-s-china-economic-analyses-of-urban-congestion/U.S.-China economic analyses of urban congestion: What both countries can learn from each otherhttp://webfeeds.brookings.edu/~/209562440/0/brookingsrss/topics/transportation~USChina-economic-analyses-of-urban-congestion-What-both-countries-can-learn-from-each-other/
Mon, 10 Oct 2016 10:10:50 +0000https://www.brookings.edu/?post_type=event&p=336426

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With the fast pace of urbanization process, countries have now come to realize that congestion delays may affect specific sectors and even the overall economy. In a research conducted by Clifford Winston at Brookings Institution and Quentin Karpilow from Yale University, the scholars explored how congestion affects the California economy accounting for the growth in employment, GDP, wages, and freight flows. Moreover, they shed lights on what China may learn from this U.S. example.

The Brookings-Tsinghua Center hosted a two-day conference on U.S.-China economic analyses of urban congestion: What both countries can learn from Each Other. The conference started from Monday, 17 October 2016 at Room 302, School of Public Policy and Management, Tsinghua University and lasted until Tuesday, 18 October 2016.

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With the fast pace of urbanization process, countries have now come to realize that congestion delays may affect specific sectors and even the overall economy. In a research conducted by Clifford Winston at Brookings Institution and Quentin Karpilow from Yale University, the scholars explored how congestion affects the California economy accounting for the growth in employment, GDP, wages, and freight flows. Moreover, they shed lights on what China may learn from this U.S. example.
The Brookings-Tsinghua Center hosted a two-day conference on U.S.-China economic analyses of urban congestion: What both countries can learn from Each Other. The conference started from Monday, 17 October 2016 at Room 302, School of Public Policy and Management, Tsinghua University and lasted until Tuesday, 18 October 2016. With the fast pace of urbanization process, countries have now come to realize that congestion delays may affect specific sectors and even the overall economy. In a research conducted by Clifford Winston at Brookings Institution and Quentin Karpilow ... https://www.brookings.edu/book/the-economic-effects-of-airline-deregulation/The Economic Effects of Airline Deregulationhttp://webfeeds.brookings.edu/~/204763174/0/brookingsrss/topics/transportation~The-Economic-Effects-of-Airline-Deregulation/
Thu, 29 Sep 2016 15:50:33 +0000https://www.brookings.edu/?post_type=book&p=327580

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In 1938 the U.S. Government took under its wing an infant airline industry. Government agencies assumed responsibility not only for airline safety but for setting fares and determining how individual markets would be served. Forty years later, the Airline Deregulation Act of 1978 set in motion the economic deregulation of the industry and opened it to market competition.

This study by Steven Morrison and Clifford Winston analyzes the effects of deregulation on both travelers and the airline industry. The authors find that lower fares and better service have netted travelers some $6 billion in annual benefits, while airline earnings have increased by $2.5 billion a year. Morrison and Winston expect still greater benefits once the industry has had time to adjust its capital structure to the unregulated marketplace, and they recommend specific public polices to ensure healthy competition.

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In 1938 the U.S. Government took under its wing an infant airline industry. Government agencies assumed responsibility not only for airline safety but for setting fares and determining how individual markets would be served. Forty years later, the Airline Deregulation Act of 1978 set in motion the economic deregulation of the industry and opened it to market competition.
This study by Steven Morrison and Clifford Winston analyzes the effects of deregulation on both travelers and the airline industry. The authors find that lower fares and better service have netted travelers some $6 billion in annual benefits, while airline earnings have increased by $2.5 billion a year. Morrison and Winston expect still greater benefits once the industry has had time to adjust its capital structure to the unregulated marketplace, and they recommend specific public polices to ensure healthy competition. In 1938 the U.S. Government took under its wing an infant airline industry. Government agencies assumed responsibility not only for airline safety but for setting fares and determining how individual markets would be served.https://www.brookings.edu/blog/techtank/2016/09/20/new-paper-examines-the-promise-and-policy-of-driverless-cars/New paper examines the promise and policy of driverless carshttp://webfeeds.brookings.edu/~/200174192/0/brookingsrss/topics/transportation~New-paper-examines-the-promise-and-policy-of-driverless-cars/
Tue, 20 Sep 2016 19:32:45 +0000https://www.brookings.edu/?p=332689

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The Department of Transportation and the National Highway Traffic and Safety Administration released long-awaited policy guidance for autonomous vehicles on September 19. The new policy seeks to harmonize rules for states and driverless car developers in a way that promotes both public safety and innovation. In a new paper, Darrell West discusses the societal benefits of driverless cars as well as the technical and regulatory barriers to their adoption. The technology has the potential to reduce traffic congestion, air pollution, and traffic fatalities, yet driverless cars remain susceptible to bad weather conditions, digital hacking, and limited wireless spectrum. West also analyzes the regulatory landscape in China, Japan, Korea, Europe, and the United States and recommends policies to pave the way for driverless cars.

The World Economic Forum predicts that driverless cars will generate an additional $67 billion in auto industry revenue while providing $3.1 trillion in societal benefits. Driverless cars can improve traffic flows by communicating with other vehicles and smart infrastructure, cutting down on commute times and air pollution created by personal vehicles. Urban traffic is further reduced by cars that can park themselves: the economist Donald Shoup estimates that 30 percent of traffic in metropolitan areas results from drivers looking for parking. Driverless cars would also enhance personal mobility in countries with aging populations that can no longer drive by themselves.

To operate without a human driver, autonomous vehicles are outfit with an array of sensors that collect data on their surroundings. Cameras take pictures of road markings, and LiDAR sensors scan for approaching vehicles and other obstacles. Roads must have clearly defined lane markings and signage, while high-definition maps of road networks update cars on their precise location and changing road conditions. Bad weather, poor infrastructure, or inaccurate maps can make navigation more difficult. Driverless cars will also need adequate wireless spectrum to communicate with each other and with smart traffic signals, as well as cybersecurity protections to prevent the hacking of these systems.

According to West’s paper, regulatory hurdles for driverless vehicles are numerous and revolve around collecting data for maps, testing cars on roads, and manufacturing restrictions—among other challenges. China benefits from national oversight of driverless cars, but still bans road testing and the collection of high-resolution data for maps. Similarly, data protection and privacy rules in the EU would make it difficult for companies to use data collected by driverless cars. State laws in the U.S. mandate a steering wheel, brakes, and a licensed driver behind the wheel, contradicting some vehicle designs that forgo all these features. According to West, countries seeking to adopt driverless cars should strive for clarity in their regulations.

Questions remain about how driverless cars would act to avoid collisions and who would be at fault when they do occur. Are the advanced algorithms that guide cars equipped to choose between the safety of its passengers, pedestrians, and other vehicles in the case of an emergency? Meanwhile, automotive insurers are determining whether the owner or the manufacturer holds liability for a driverless car. These questions will become increasingly important now that automakers plan to produce driverless cars as early as 2021. This month, Uber has already begun testing driverless cars (with human drivers ready to take over) for its rideshare service in Pittsburgh.

Soon, people around the world will have driverless options for taking them safely to their destinations, which is why, West writes, “It’s essential for leaders to provide reasonable guidance on how to commercialize advanced technologies in transportation.” Once those technologies have matured and such regulations are finalized, it will be public acceptance that ultimately impacts how quickly and widely driverless cars are adopted.

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The Department of Transportation and the National Highway Traffic and Safety Administration released long-awaited policy guidance for autonomous vehicles on September 19. The new policy seeks to harmonize rules for states and driverless car developers in a way that promotes both public safety and innovation. In a new paper, Darrell West discusses the societal benefits of driverless cars as well as the technical and regulatory barriers to their adoption. The technology has the potential to reduce traffic congestion, air pollution, and traffic fatalities, yet driverless cars remain susceptible to bad weather conditions, digital hacking, and limited wireless spectrum. West also analyzes the regulatory landscape in China, Japan, Korea, Europe, and the United States and recommends policies to pave the way for driverless cars.
The World Economic Forum predicts that driverless cars will generate an additional $67 billion in auto industry revenue while providing $3.1 trillion in societal benefits. Driverless cars can improve traffic flows by communicating with other vehicles and smart infrastructure, cutting down on commute times and air pollution created by personal vehicles. Urban traffic is further reduced by cars that can park themselves: the economist Donald Shoup estimates that 30 percent of traffic in metropolitan areas results from drivers looking for parking. Driverless cars would also enhance personal mobility in countries with aging populations that can no longer drive by themselves.
To operate without a human driver, autonomous vehicles are outfit with an array of sensors that collect data on their surroundings. Cameras take pictures of road markings, and LiDAR sensors scan for approaching vehicles and other obstacles. Roads must have clearly defined lane markings and signage, while high-definition maps of road networks update cars on their precise location and changing road conditions. Bad weather, poor infrastructure, or inaccurate maps can make navigation more difficult. Driverless cars will also need adequate wireless spectrum to communicate with each other and with smart traffic signals, as well as cybersecurity protections to prevent the hacking of these systems.
According to West’s paper, regulatory hurdles for driverless vehicles are numerous and revolve around collecting data for maps, testing cars on roads, and manufacturing restrictions—among other challenges. China benefits from national oversight of driverless cars, but still bans road testing and the collection of high-resolution data for maps. Similarly, data protection and privacy rules in the EU would make it difficult for companies to use data collected by driverless cars. State laws in the U.S. mandate a steering wheel, brakes, and a licensed driver behind the wheel, contradicting some vehicle designs that forgo all these features. According to West, countries seeking to adopt driverless cars should strive for clarity in their regulations.
Questions remain about how driverless cars would act to avoid collisions and who would be at fault when they do occur. Are the advanced algorithms that guide cars equipped to choose between the safety of its passengers, pedestrians, and other vehicles in the case of an emergency? Meanwhile, automotive insurers are determining whether the owner or the manufacturer holds liability for a driverless car. These questions will become increasingly important now that automakers plan to produce driverless cars as early as 2021. This month, Uber has already begun testing driverless cars (with human drivers ready to take over) for its rideshare service in Pittsburgh.
Soon, people around the world will have driverless options for taking them safely to their destinations, which is why, West writes, “It’s essential for leaders to provide reasonable guidance on how to commercialize advanced technologies in transportation.” Once those technologies have matured and such ... The Department of Transportation and the National Highway Traffic and Safety Administration released long-awaited policy guidance for autonomous vehicles on September 19. The new policy seeks to harmonize rules for states and driverless car ... https://www.brookings.edu/research/transportation-network-companies-present-challenges-and-opportunities-in-asias-booming-cities/Transportation network companies present challenges and opportunities in Asia’s booming citieshttp://webfeeds.brookings.edu/~/193626658/0/brookingsrss/topics/transportation~Transportation-network-companies-present-challenges-and-opportunities-in-Asia%e2%80%99s-booming-cities/
Thu, 08 Sep 2016 19:52:00 +0000https://www.brookings.edu/?post_type=research&p=330590

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Since the advent of the automobile roughly a century ago, there has been relatively little innovation in the way people move around cities and suburbs. That relative stasis changed just a few short years ago. Transportation network companies (TNCs), an industry that began as a set of startups in San Francisco and Silicon Valley, have created a viable travel alternative in many places worldwide. As long as smartphones, GPS navigation, and roads exist, TNCs can offer shared rides in any urban context.

This is certainly the case in South and Southeast Asia, where cities from Beijing to New Delhi prove that emerging economies are an especially fertile ground to adopt new urban mobility models. Surging populations and growing economic clout have made Asian cities big business for TNCs. The recent $8-billion merger between Uber China and Didi Chuxing is just the latest seismic shift in a fast-growing, unstable marketplace.

Yet while the business community continues to focus on ridership counts, fundraising rounds, and cutthroat competition, the larger impacts on urban development should not be overlooked. TNCs offer an enormous opportunity to better connect Asian households to economic opportunity, but that opportunity will only be fulfilled if TNCs are available to all potential users and support long-run objectives related to housing affordability and well-connected neighborhoods.

Urban and spatial context

Supersized urbanization across South and Southeast Asia not only drives business growth for TNCs—it also serves as ground zero for major urban challenges facing the entire planet. While emerging Asian economies urbanized later than peers in Europe, North America, and even Latin America, they have quickly made up the difference—and likely represent a key segment of users to take advantage of TNCs in years to come. In 1980, China had an urban population that equaled that of the United States and Canada combined; in 2015, its urban population was three times that size. Similarly scaled growth occurred in Southern Asia—led primarily by India—and Southeast Asia. Even more impressive, these countries will house an additional 1 billion urban residents by 2050.

Many of these new urban dwellers—whether due to rural migration or natural birth—will reside in “megacities,” or metropolitan areas with over 10 million people. The continent is already home to more megacities than any other, 14 of which are in emerging economies. Another seven cities in emerging Asian economies will join the ranks by 2030. Overall, that’s an enormous pool of potential customers today and a promise of even more customers tomorrow.

Fortunately, Asia’s urban population is also growing wealthier. In aggregate, many of these metro areas still lag behind places like New York and London, but the Brookings Global Metro Monitor shows how Asia’s largest metro areas experienced significantly higher GDP-per-capita growth between 2000 and 2014. In most cases, GDP per capita grew faster in metro areas than their overall countries—signifying the growing economic clout of city versus rural dwellers. And all of that new wealth creates real purchasing power for the higher-earning individuals living in cities.

However, this combination of urban population growth and greater wealth also introduces significant spatial challenges. Asian cities continue to grow outward and consume more land, even if their overall densities are increasing. But unlike Europe and North America, Asian sprawl is typically more chaotic and often reserves less space for transportation. China is the exception, but even their past preference for superblocks incentivizes sprawl. As NYU researcher Shlomo Angel and his colleagues pointed out back in 2005, growing distances and limited transportation infrastructure would limit mobility, and therefore access, in emerging cities.

It should not come as a surprise, then, that motorized transportation—whether through private cars or motorcycles—is now more than just a symbol of wealth in emerging economies; it is quickly becoming a necessity to reach jobs and other places of interest. For instance, China’s car ownership rate is 25 cars per 100 people—to compare, Italians have 60 cars per 100 people—but it added 23 million new cars to the road in 2015 alone. The Philippines, India, and Laos are following China’s car ownership push. Meanwhile, a Pew Research Center survey found over 80 percent of people in Thailand, Vietnam, Indonesia, and Malaysia own a motorbike.

The issue is where to put all these vehicles. Not only do Asian cities now dominate the top rankings of TomTom’s congestion index, these hypercongested places are also seeing their numbers get worse. The economic cost associated with lost productivity and time is also enormous, equaling two to five percent of aggregate GDP. Meanwhile, local commute times are getting even longer. While average commutes in London and Tokyo are below 40 minutes, Beijing’s and Shanghai’s already exceed 50 minutes. Anyone who has traveled to megacities like Bangkok and Jakarta, Indonesia, has a vivid picture of streets bursting at the seams. Finally, parking is a growing concern in Asian cities, as storing all those vehicles threatens to consume land that is in increasingly short supply.

Gauging the potential impact of TNCs

Combined, these urbanization trends create business opportunities for TNCs and economic opportunities for cities. The swelling middle class in Asian cities offer TNCs a growing customer base—a marked contrast with more developed cities where stalled population growth forces a focus on greater market penetration. At the same time, for every private driver that can switch to a shared TNC service, the host city theoretically benefits by removing a current or future vehicle from the road and related demand for parking space.

The business opportunity is already real. China’s largest ridesourcing company, Didi Chuxing, provided over 1.4 billion rides in 2015 through a combination of taxi hailing, private chauffeurs, pooled rides, and even buses. Grab offers a mix of taxi and motorbike services across six Asian countries. Go-jek focuses exclusively on motorbikes in Indonesian cities and averaged 340,000 bookings per day in January 2016. Ola is the homegrown entrant in India, with a significant but uncertain market presence. Meanwhile, Uber competes in all of these countries (although their Chinese presence is changing due to the Didi merger), although not always in the same cities.

Financial markets have responded to this business growth with a major vote of investor confidence. Even before the Uber China merger, Didi landed an enormous $7-billion private investment in 2016. Ola raised $500 million in late 2015 and is reportedly set for a $1-billion fundraising round this summer. Grab has already raised $680 million, while Go-Jek reportedly closed a fresh $550 million in new funding. The numbers are staggering; tracking them is almost dizzying.

For city leaders, though, it would be easy to get distracted by the eye-popping numbers and simply let the marketplace develop on its own (or try to stop it altogether). Either approach would be a mistake. Instead, government officials should recognize market demand for TNC services and consider how it can be leveraged for a more focused civic goal: better connecting people to economic opportunity.

On this count, work is far from complete.

First, not all residents can access and afford TNC services. Smartphone penetration in Asian cities is far from ubiquitous. China and Malaysia have the highest figures around 60 percent, but other emerging nations like India and Indonesia are closer to 20 percent (although the market is growing rapidly). Even if an individual owns a smartphone, there’s no guarantee they have a mobile data package to support ride hailing in any location. But it’s the cost of the rides that may be the biggest barrier. Even with privately-subsidized fares and growing incomes in the region, it still may be too expensive to rely on TNCs for ubiquitous urban mobility.

Consider motorbike travel in India. The country’s average GDP per capita in current dollars is $1,120 (USD) a year. Ola’s motorcycle taxies charge a rate of two rupees per kilometer and one rupee per minute with a minimum 30-rupee fare—meaning that the service targets those with commuting distances that exceed the five-kilometer national average. Taking an Ola motorcycle to and from work every day—assuming 250 working days per year—at the minimum fare would cost a commuter roughly $250 USD a year, or 22 percent of an average income. What if a person’s commute exceeds the minimum, or a user wants to take additional trips? Those may be affordable rates for India’s wealthier individuals, but they’re an absolute barrier to the average Indian worker.

There are also very real concerns about whether TNCs will only intensify demand for urban sprawl. While early research finds that ridesharing can reduce vehicle ownership and overall driving, those results derive from developed countries. There are still major questions about whether Asian TNCs can have the same effect. Do TNCs induce people to drive less or do they shift current pedestrians and bike riders to motorized alternatives? Do TNCs make suburban housing more attractive to Asian families, thereby leading to more aggregate driving? As Asian cities confront the costs of sprawl, they need to closely monitor TNCs’ related impacts.

Potential economic opportunities and steps toward better planning

Yet even with these challenges, TNCs offer great opportunity for Asian cities.

First, city leaders need to respond with innovative transportation policies. The long-term answer can’t simply be a blanket on-or-off switch like the situations in Bangalore and Thailand. Instead, Asian regulators should follow the lead of many North American cities and consider a broad range of public policies that seek to collaborate with private-sector TNCs to deliver shared public benefits, especially around access to economic opportunity. It could mean offering public subsidies to TNCs if they connect people to fixed route transit, which could shorten TNC trips, boost transit ridership, and reduce congestion in the urban core. Similar subsidies could apply if TNCs bring residents to vital economic sites like hospitals or schools. Governments could enter data-sharing agreements with TNCs, trading the right to operate in the city for better information about how people move within it. Other policies could promote TNC usage while increasing costs for solo driving, along the lines of congestion charges. In short, cities need to try to be as innovative in their transportation policymaking as the TNCs are in designing new mobility solutions.

Second, cities should consider TNCs a tool to deliver the built environment they would like to see. This isn’t as cut and dry as only building compact cities or only promoting peripheral development. It’s about considering how TNCs will impact land use and whether related policies should adjust. Presumably, higher TNC usage would require less space for parking and even less space for future roadway expansions. Cities must begin to consider how that land could be repurposed, especially to promote transportation access. A similar planning process should involve housing policies, whether that’s the need for more multifamily housing in denser nodes; increased demand for suburban, single-family housing; or managing urban slums. Ideally, the growth in TNCs can motivate construction of more affordable housing and ameliorate spatial mismatches.

Third, there’s no more powerful way to give people access to TNC services then to get them connected to the digital economy. That means ensuring all residents—regardless of income—have access to transportation services like TNCs whose full functionality are only available to those with a smartphone and a mobile data plan. Not only would boosting smartphone penetration help achieve this goal, it has the added benefit of giving people experience with digital skills, facilitating access to services like mobile banking and distance education, and supporting a transition to e-governance. The potential spillovers are enormous.

Like the rest of the world, all signs point toward TNCs attracting more customers and leading an overall evolution around how urban dwellers in Asia’s emerging economies use digital technology to share resources. Yet the growth of ride hailing is about more than just mobility as a business—fundamentally, urban transportation is about getting people to where they need to go, ideally at an affordable price. TNCs have the opportunity to deliver that kind of accessibility in Asia’s cities, but delivering on that promise will only be possible with purposeful public policy.

Celia Healy contributed to this piece.

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https://www.brookings.edu/wp-content/uploads/2016/09/metro_20160908_chinacity.jpg?w=240Since the advent of the automobile roughly a century ago, there has been relatively little innovation in the way people move around cities and suburbs. That relative stasis changed just a few short years ago. Transportation network companies (TNCs), an industry that began as a set of startups in San Francisco and Silicon Valley, have created a viable travel alternative in many places worldwide. As long as smartphones, GPS navigation, and roads exist, TNCs can offer shared rides in any urban context.
This is certainly the case in South and Southeast Asia, where cities from Beijing to New Delhi prove that emerging economies are an especially fertile ground to adopt new urban mobility models. Surging populations and growing economic clout have made Asian cities big business for TNCs. The recent $8-billion merger between Uber China and Didi Chuxing is just the latest seismic shift in a fast-growing, unstable marketplace.
Yet while the business community continues to focus on ridership counts, fundraising rounds, and cutthroat competition, the larger impacts on urban development should not be overlooked. TNCs offer an enormous opportunity to better connect Asian households to economic opportunity, but that opportunity will only be fulfilled if TNCs are available to all potential users and support long-run objectives related to housing affordability and well-connected neighborhoods.
Urban and spatial context
Supersized urbanization across South and Southeast Asia not only drives business growth for TNCs—it also serves as ground zero for major urban challenges facing the entire planet. While emerging Asian economies urbanized later than peers in Europe, North America, and even Latin America, they have quickly made up the difference—and likely represent a key segment of users to take advantage of TNCs in years to come. In 1980, China had an urban population that equaled that of the United States and Canada combined; in 2015, its urban population was three times that size. Similarly scaled growth occurred in Southern Asia—led primarily by India—and Southeast Asia. Even more impressive, these countries will house an additional 1 billion urban residents by 2050.
Many of these new urban dwellers—whether due to rural migration or natural birth—will reside in “megacities,” or metropolitan areas with over 10 million people. The continent is already home to more megacities than any other, 14 of which are in emerging economies. Another seven cities in emerging Asian economies will join the ranks by 2030. Overall, that’s an enormous pool of potential customers today and a promise of even more customers tomorrow.
Fortunately, Asia’s urban population is also growing wealthier. In aggregate, many of these metro areas still lag behind places like New York and London, but the Brookings Global Metro Monitor shows how Asia’s largest metro areas experienced significantly higher GDP-per-capita growth between 2000 and 2014. In most cases, GDP per capita grew faster in metro areas than their overall countries—signifying the growing economic clout of city versus rural dwellers. And all of that new wealth creates real purchasing power for the higher-earning individuals living in cities.
However, this combination of urban population growth and greater wealth also introduces significant spatial challenges. Asian cities continue to grow outward and consume more land, even if their overall densities are increasing. But unlike Europe and North America, Asian sprawl is typically more chaotic and often reserves less space for transportation. China is the exception, but even their past preference for superblocks incentivizes sprawl. As NYU researcher Shlomo Angel and his colleagues pointed out back in 2005, growing distances and limited transportation infrastructure would limit mobility, and therefore access, in emerging cities.
It should not come as a surprise, ... Since the advent of the automobile roughly a century ago, there has been relatively little innovation in the way people move around cities and suburbs. That relative stasis changed just a few short years ago. Transportation network companies (TNCs)https://www.brookings.edu/podcast-episode/inclusive-cities-transportation-and-accessibility/Inclusive cities: Transportation and accessibilityhttp://webfeeds.brookings.edu/~/189053408/0/brookingsrss/topics/transportation~Inclusive-cities-Transportation-and-accessibility/
Wed, 31 Aug 2016 17:30:22 +0000https://www.brookings.edu/?post_type=podcast-episode&p=329624

“Access thinking is all about how can we connect people better to those critical destinations they need to get to, and do so in a way ideally that works for everyone with different ranges of incomes, that does so in a way that doesn’t endlessly consume all of the land that’s available, and doing it in a way that maintaining all that infrastructure, that it’s fiscally sustainable,” Tomer explains.

“Because of technology, because of being able to deal with metadata now,” Gutman says, “we’re actually able to measure and report much more inexpensively than we had in the past. I think that’s really a major breakthrough. Now is the time to be able to do this, because we have the instruments and the modeling at hand to be able to move in a very big way toward a measure of accessibility.”

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Adie Tomer, fellow in the Metropolitan Policy Program, and Jeffrey Gutman, senior fellow in Global Economy and Development, discuss how to transform transportation policy with a focus on accessibility and how cities around the world are grappling with improving infrastructure and increasing access for people of all incomes.
“Access thinking is all about how can we connect people better to those critical destinations they need to get to, and do so in a way ideally that works for everyone with different ranges of incomes, that does so in a way that doesn’t endlessly consume all of the land that’s available, and doing it in a way that maintaining all that infrastructure, that it’s fiscally sustainable,” Tomer explains.
“Because of technology, because of being able to deal with metadata now,” Gutman says, “we’re actually able to measure and report much more inexpensively than we had in the past. I think that’s really a major breakthrough. Now is the time to be able to do this, because we have the instruments and the modeling at hand to be able to move in a very big way toward a measure of accessibility.”
Related links:
Moving to access
Urban equality and access: Will Habitat III rise to the challenge?
Shifting gears to a new transportation model
Building smart cities in India
With thanks to audio engineer Mark Hoelscher, Vanessa Sauter, Fred Dews, and Richard Fawal.
Questions? Comments? Email us at intersections@brookings.edu. Adie Tomer, fellow in the Metropolitan Policy Program, and Jeffrey Gutman, senior fellow in Global Economy and Development, discuss how to transform transportation policy with a focus on accessibility and how cities around the world are ... https://www.brookings.edu/research/what-recessions-teach-us-about-preventing-traffic-deaths-and-why-we-need-driverless-cars/What recessions teach us about preventing traffic deaths—and why we need driverless carshttp://webfeeds.brookings.edu/~/185644776/0/brookingsrss/topics/transportation~What-recessions-teach-us-about-preventing-traffic-deaths%e2%80%94and-why-we-need-driverless-cars/
Thu, 25 Aug 2016 13:59:37 +0000https://www.brookings.edu/?post_type=research&p=328891

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Recovery from the Great Recession has brought more jobs, lower unemployment, and economic growth. But it’s also ushered in a much less welcome statistic: a rise in deaths due to traffic accidents. Are the two related?

New research to be published in the forthcoming issue of the Journal of Risk and Uncertainty by Brookings Senior Fellow Clifford Winston and the University of Houston’s Vikram Maheshri suggests they are—and that there are important takeaways from understanding that relationship.

In “Did the Great Recession keep bad drivers off the road?” Winston and Maheshri illustrate that traffic deaths decline during recessions (thus the rise when the economy recovers). They find that for every one percentage point increase in unemployment during the Great Recession, there was a 14 percent reduction in traffic fatalities—or about 5,000 fewer deaths per year.

Why traffic deaths decline during a recession

Economists have known for some time that traffic deaths decline during recessions, but Winston’s and Maheshri’s paper is one of the first studies to explain why. Using a novel dataset that followed individual drivers, their study revealed what publically-available, aggregate data couldn’t.

As it turns out, recessions don’t necessarily affect how many miles the average driver drives, but they do change the composition of drivers on the road. As unemployment rises, more dangerous drivers drive less, while safer drivers drive more.

What we can learn from the research

The paper’s findings are significant themselves, but as Winston and Maheshri point out: they should also inform policy solutions to traffic deaths.

If we know getting riskier drivers off the road can have such a big impact, then we should look for ways to do that. One readily available solution? Driverless cars.

“During the transition from human drivers to driverless cars, policymakers could allow the most dangerous drivers, who ordinarily might have their driver’s licenses suspended or even revoked following a serious driving violation…to continue to have access to an automobile provided it is driverless…this would expedite the transition to driverless cars and help educate the public and build trust in a new technology,” Winston and Maheshri write.

Expediting the adoption of driverless cars could save thousands of lives each year.

A win-win for drivers and the economy

While saving human lives is and should be the most important goal, there are also real benefits to the economy of reducing traffic deaths.

In their paper, Winston and Maheshri note that total savings from the reduction in traffic accidents and fatalities can be estimated annually at tens of billions of dollars when accounting for the value of life and limb, travel delays, and the costs of traffic repairs.

Driverless cars have the potential to be a win-win, for drivers and the economy as a whole.

Winston and Maheshri conclude their paper with this note:

“With the transition to driverless cars eventually complete, the risk among drivers would be eliminated. To be sure, automobile accidents, even fatal ones, might still occur. But that would post a technological instead of a human problem, which our society has historically found much easier to solve.”

For more on the study’s major findings, watch the animation below:

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https://www.brookings.edu/wp-content/uploads/2016/08/traffic-photo.jpg?w=270Recovery from the Great Recession has brought more jobs, lower unemployment, and economic growth. But it’s also ushered in a much less welcome statistic: a rise in deaths due to traffic accidents. Are the two related?
New research to be published in the forthcoming issue of the Journal of Risk and Uncertainty by Brookings Senior Fellow Clifford Winston and the University of Houston’s Vikram Maheshri suggests they are—and that there are important takeaways from understanding that relationship.
In “Did the Great Recession keep bad drivers off the road?” Winston and Maheshri illustrate that traffic deaths decline during recessions (thus the rise when the economy recovers). They find that for every one percentage point increase in unemployment during the Great Recession, there was a 14 percent reduction in traffic fatalities—or about 5,000 fewer deaths per year.
Why traffic deaths decline during a recession
Economists have known for some time that traffic deaths decline during recessions, but Winston’s and Maheshri’s paper is one of the first studies to explain why. Using a novel dataset that followed individual drivers, their study revealed what publically-available, aggregate data couldn’t.
As it turns out, recessions don’t necessarily affect how many miles the average driver drives, but they do change the composition of drivers on the road. As unemployment rises, more dangerous drivers drive less, while safer drivers drive more.
What we can learn from the research
The paper’s findings are significant themselves, but as Winston and Maheshri point out: they should also inform policy solutions to traffic deaths.
If we know getting riskier drivers off the road can have such a big impact, then we should look for ways to do that. One readily available solution? Driverless cars.
“During the transition from human drivers to driverless cars, policymakers could allow the most dangerous drivers, who ordinarily might have their driver’s licenses suspended or even revoked following a serious driving violation…to continue to have access to an automobile provided it is driverless…this would expedite the transition to driverless cars and help educate the public and build trust in a new technology,” Winston and Maheshri write.
Expediting the adoption of driverless cars could save thousands of lives each year.
A win-win for drivers and the economy
While saving human lives is and should be the most important goal, there are also real benefits to the economy of reducing traffic deaths.
In their paper, Winston and Maheshri note that total savings from the reduction in traffic accidents and fatalities can be estimated annually at tens of billions of dollars when accounting for the value of life and limb, travel delays, and the costs of traffic repairs.
Driverless cars have the potential to be a win-win, for drivers and the economy as a whole.
Winston and Maheshri conclude their paper with this note:
“With the transition to driverless cars eventually complete, the risk among drivers would be eliminated. To be sure, automobile accidents, even fatal ones, might still occur. But that would post a technological instead of a human problem, which our society has historically found much easier to solve.”
For more on the study's major findings, watch the animation below:
Recovery from the Great Recession has brought more jobs, lower unemployment, and economic growth. But it’s also ushered in a much less welcome statistic: a rise in deaths due to traffic accidents. Are the two related?https://www.brookings.edu/blog/the-avenue/2016/08/22/auto-slowdown-signals-narrowing-of-advanced-sector-growth/Auto slowdown signals narrowing of advanced-sector growthhttp://webfeeds.brookings.edu/~/183904282/0/brookingsrss/topics/transportation~Auto-slowdown-signals-narrowing-of-advancedsector-growth/
Mon, 22 Aug 2016 18:50:40 +0000https://www.brookings.edu/?p=328479

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Hazards ahead: That’s what data about the auto sector are signaling. Economic leaders and workers may want to buckle up.

Caution lights are definitely blinking for the auto industry – and the manufacturing sector more generally. This month, Metro’s new update on growth and change in America’s 50 critical advanced industries showed that output growth slowed markedly in the three major auto industries during the last two years while employment growth – still positive – leveled off.

Meanwhile, sales for the three top automakers selling in the U.S. slipped in July as the strong growth rate that has defined the past six years slowed to a crawl – another indication that growth is plateauing after a six-year boom. A few weeks ago Ford jolted the industry by saying sales have peaked and projecting a slower fall and a tough 2017.

This is a sea change, given the boom since 2010, and it matters a lot right now because auto is critical to the manufacturing sector, which itself matters enormously to the U.S. growth in manufacturing-oriented metropolitan areas.

Auto-related industries, after all, delivered fully 70 percent of the nation’s advanced manufacturing employment growth during the last two years, given the slow growth other manufacturing industries experienced in the face of a strong dollar and global headwinds. In keeping with that, the sector has become a crucial source of innovation, output, productivity, and new jobs (90,000+ “direct” ones in the last two years).

As such, auto industries – with their long supply chains – contribute hugely to the prosperity of something like a quarter of the nation’s largest metropolitan areas, arrayed along a corridor sweeping from the Great Lakes states into Kentucky, Tennessee, South Carolina, and Alabama. Immediately below you can see the distribution of large metro areas with elevated shares of their employment in auto. It’s a significant swath of the country’s Midwestern and Southern heartland.

All of which underscores that a further slowing of the auto sector – as appears to be beginning – could be an unfortunate development for the nation’s steady but uninspiring economic expansion. The auto industry’s recovery has been a bright spot for the U.S. economy, with the three major auto industries and four digital services delivering two-thirds of the nation’s vital and sustaining advanced-sector growth. If that goes away, the nation’s advanced sector and multiple states and metro areas will have to contend with a further narrowing of an advanced-sector growth base that is already too narrow.

Losing auto as a strong job creator would for one thing take away one of the nation’s few sizable sources of well-paying jobs for workers without a BA. The auto industry and other manufacturing industries are notable for continuing to pay decent wages to less-educated workers. The nation needs more growth from its most accessible high-value industries, not less! Similarly, a slowing of the auto sector would narrow the geography of growth across multiple heartland states and metropolitan areas. More dots would turn pink or red on the advanced industry employment growth map as hiring slowed. More regions would lose direct jobs, supplier jobs, and consumer spending

In that vein, a flattening of growth in the auto sectors could well emerge as a substantial issue in the first year of a new president’s administration. Momentum should keep hiring going through the election and keep a plateauing auto sector from worsening the distress already agitating the electorate in “battleground” states like Michigan and Ohio. But should auto growth slump and the mediocre 1.2 percent a year growth rate of the manufacturing sector slip further, the new president is going to be forced to address the status of the nation’s advanced-industries sector. Further slowing will make shoring up and expanding the advanced-industries sector even more imperative than it already is.

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https://www.brookings.edu/wp-content/uploads/2016/08/metro_20160822_advancedindustryautoshareheader.jpg?w=280Hazards ahead: That’s what data about the auto sector are signaling. Economic leaders and workers may want to buckle up.
Caution lights are definitely blinking for the auto industry – and the manufacturing sector more generally. This month, Metro’s new update on growth and change in America’s 50 critical advanced industries showed that output growth slowed markedly in the three major auto industries during the last two years while employment growth – still positive – leveled off.
Meanwhile, sales for the three top automakers selling in the U.S. slipped in July as the strong growth rate that has defined the past six years slowed to a crawl – another indication that growth is plateauing after a six-year boom. A few weeks ago Ford jolted the industry by saying sales have peaked and projecting a slower fall and a tough 2017.
This is a sea change, given the boom since 2010, and it matters a lot right now because auto is critical to the manufacturing sector, which itself matters enormously to the U.S. growth in manufacturing-oriented metropolitan areas.
Auto-related industries, after all, delivered fully 70 percent of the nation’s advanced manufacturing employment growth during the last two years, given the slow growth other manufacturing industries experienced in the face of a strong dollar and global headwinds. In keeping with that, the sector has become a crucial source of innovation, output, productivity, and new jobs (90,000+ “direct” ones in the last two years).
As such, auto industries – with their long supply chains – contribute hugely to the prosperity of something like a quarter of the nation’s largest metropolitan areas, arrayed along a corridor sweeping from the Great Lakes states into Kentucky, Tennessee, South Carolina, and Alabama. Immediately below you can see the distribution of large metro areas with elevated shares of their employment in auto. It’s a significant swath of the country’s Midwestern and Southern heartland.
All of which underscores that a further slowing of the auto sector – as appears to be beginning – could be an unfortunate development for the nation’s steady but uninspiring economic expansion. The auto industry’s recovery has been a bright spot for the U.S. economy, with the three major auto industries and four digital services delivering two-thirds of the nation’s vital and sustaining advanced-sector growth. If that goes away, the nation’s advanced sector and multiple states and metro areas will have to contend with a further narrowing of an advanced-sector growth base that is already too narrow.
Losing auto as a strong job creator would for one thing take away one of the nation’s few sizable sources of well-paying jobs for workers without a BA. The auto industry and other manufacturing industries are notable for continuing to pay decent wages to less-educated workers. The nation needs more growth from its most accessible high-value industries, not less! Similarly, a slowing of the auto sector would narrow the geography of growth across multiple heartland states and metropolitan areas. More dots would turn pink or red on the advanced industry employment growth map as hiring slowed. More regions would lose direct jobs, supplier jobs, and consumer spending
In that vein, a flattening of growth in the auto sectors could well emerge as a substantial issue in the first year of a new president’s administration. Momentum should keep hiring going through the election and keep a plateauing auto sector from worsening the distress already agitating the electorate in “battleground” states like Michigan and Ohio. But should auto growth slump and the mediocre 1.2 percent a year growth rate of the manufacturing sector slip further, the new president is going to be forced to address the status of the nation’s ... Hazards ahead: That’s what data about the auto sector are signaling. Economic leaders and workers may want to buckle up.
Caution lights are definitely blinking for the auto industry – and the manufacturing sector more generally.https://www.brookings.edu/blog/techtank/2016/08/18/autopilot-fatality-reveals-risks-of-technology-testing/Autopilot fatality reveals risks of technology testinghttp://webfeeds.brookings.edu/~/181571456/0/brookingsrss/topics/transportation~Autopilot-fatality-reveals-risks-of-technology-testing/
Thu, 18 Aug 2016 12:54:52 +0000https://www.brookings.edu/?p=327875

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In May, a semi-autonomous car was involved in the first fatal crash of its kind when a Tesla Model S operating in Autopilot mode and its driver failed to recognize a white semi-truck turning left across the highway ahead of the car. The driver was killed when the car passed under the trailer of the truck and careened off the road. This accident presents challenges for the introduction of semi-autonomous features, as transportation regulators work to maintain safety while promoting innovation. Moving forward, regulators should work with Tesla and other automotive manufacturers in order to improve driverless car technology.

The accident is currently being investigated by the National Transportation Safety Board and Tesla. Preliminary reports indicate the white trailer was not recognized by the driver or the car against “a brightly lit sky,” and the brakes were not applied. However, Tesla asserts this crash resulted not as a fault of its Autopilot system, but because of the braking system of the Model S. They argue the car either did not see the trailer or interpreted it as a building or overhead sign, objects that the system is programmed to ignore to prevent unnecessary braking. Furthermore, the driver allegedly was speeding when the crash occurred, traveling 74 miles per hour in a 65 mile per hour zone.

Regardless of the causes of the crash, this accident raises questions regarding the implementation of semi-autonomous features in vehicles. Tesla is a major innovator in this realm. Its electric Model S was updated late last year with its Autopilot features, which introduced automatic lane changes, adaptive cruise control, and side collision warning. As shown in many videos, the car can easily navigate stop and go traffic, keep the car in its lane on highways, and avoid collisions with cars. This fatality is the first in over 130 million miles where Autopilot was in use, compared to the U.S. average of one fatality every 94 million miles.

However, the Autopilot system is not fully autonomous and requires continuous attention by the driver to take over when necessary. Tesla markets the system as an active beta, software that receives updates continuously and is still far from a final version. But drivers who have used the system describe it as “the car is driving itself,” and they are not afraid to push the limits of the technology.

A survey by AAA found a majority of drivers want at least one semi-autonomous feature in their next car, yet three-quarters of drivers are still afraid of riding in fully autonomous vehicles, even though the latter may be safer due to the absence of driver distractions from automotive operations. Given public sentiment, should manufacturers wait until the technology is fully developed to reduce traffic deaths, or should they release it in stages to reduce deaths incrementally? Both incremental updates and fully developed releases will reduce the 33,000 people who die in traffic accidents each year in the United States. Ultimately, early adopters must decide for themselves if they are willing to accept reduced risk with increased responsibility, or wait for fully driverless cars.

The fatal collision of the Tesla Model S was tragic, but completely dismissing the Tesla Autopilot system as dangerous would be short-sighted. Tesla should change its marketing of Autopilot, as argued by safety groups like Consumer Reports, to better inform consumers of its limitations. Any beta system has its flaws, but it would be difficult to argue that these safety features are not worth implementing unless crashes and fatalities can be completely eliminated. For this reason, the National Highway Traffic Safety Administration, the National Transportation Safety Board, and other transportation regulators must ensure Tesla and other car companies learn from these incidents to help consumers use new technologies within their limits. Consumers, companies, and regulators can learn more together about the nature of autonomous cars and develop safer technology for future drivers.

Jacob Lineberry contributed to this post.

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In May, a semi-autonomous car was involved in the first fatal crash of its kind when a Tesla Model S operating in Autopilot mode and its driver failed to recognize a white semi-truck turning left across the highway ahead of the car. The driver was killed when the car passed under the trailer of the truck and careened off the road. This accident presents challenges for the introduction of semi-autonomous features, as transportation regulators work to maintain safety while promoting innovation. Moving forward, regulators should work with Tesla and other automotive manufacturers in order to improve driverless car technology.
The accident is currently being investigated by the National Transportation Safety Board and Tesla. Preliminary reports indicate the white trailer was not recognized by the driver or the car against “a brightly lit sky,” and the brakes were not applied. However, Tesla asserts this crash resulted not as a fault of its Autopilot system, but because of the braking system of the Model S. They argue the car either did not see the trailer or interpreted it as a building or overhead sign, objects that the system is programmed to ignore to prevent unnecessary braking. Furthermore, the driver allegedly was speeding when the crash occurred, traveling 74 miles per hour in a 65 mile per hour zone.
Regardless of the causes of the crash, this accident raises questions regarding the implementation of semi-autonomous features in vehicles. Tesla is a major innovator in this realm. Its electric Model S was updated late last year with its Autopilot features, which introduced automatic lane changes, adaptive cruise control, and side collision warning. As shown in many videos, the car can easily navigate stop and go traffic, keep the car in its lane on highways, and avoid collisions with cars. This fatality is the first in over 130 million miles where Autopilot was in use, compared to the U.S. average of one fatality every 94 million miles.
However, the Autopilot system is not fully autonomous and requires continuous attention by the driver to take over when necessary. Tesla markets the system as an active beta, software that receives updates continuously and is still far from a final version. But drivers who have used the system describe it as “the car is driving itself,” and they are not afraid to push the limits of the technology.
A survey by AAA found a majority of drivers want at least one semi-autonomous feature in their next car, yet three-quarters of drivers are still afraid of riding in fully autonomous vehicles, even though the latter may be safer due to the absence of driver distractions from automotive operations. Given public sentiment, should manufacturers wait until the technology is fully developed to reduce traffic deaths, or should they release it in stages to reduce deaths incrementally? Both incremental updates and fully developed releases will reduce the 33,000 people who die in traffic accidents each year in the United States. Ultimately, early adopters must decide for themselves if they are willing to accept reduced risk with increased responsibility, or wait for fully driverless cars.
The fatal collision of the Tesla Model S was tragic, but completely dismissing the Tesla Autopilot system as dangerous would be short-sighted. Tesla should change its marketing of Autopilot, as argued by safety groups like Consumer Reports, to better inform consumers of its limitations. Any beta system has its flaws, but it would be difficult to argue that these safety features are not worth implementing unless crashes and fatalities can be completely eliminated. For this reason, the National Highway Traffic Safety Administration, the National Transportation Safety Board, and other transportation regulators must ensure Tesla and other car companies learn from these incidents to help consumers use new technologies within their limits. Consumers, companies, and regulators can learn more ... In May, a semi-autonomous car was involved in the first fatal crash of its kind when a Tesla Model S operating in Autopilot mode and its driver failed to recognize a white semi-truck turning left across the highway ahead of the car.